Conference Proceedings

Word Representation Models for Morphologically Rich Languages in Neural Machine Translation

Ekaterina Vylomova, Trevor Cohn, Xuanli He, Gholamreza Haffari

Proceedings of the First Workshop on Subword and Character Level Models in NLP, Copenhagen, Denmark, September 7, 2017 | The Association for Computational Linguistics | Published : 2017


Out-of-vocabulary words present a great challenge for Machine Translation. Recently various character-level compositional models were proposed to address this issue. In current research we incorporate two most popular neural architectures, namely LSTM and CNN, into hard- and soft-attentional models of translation for character-level representation of the source. We propose semantic and morphological intrinsic evaluation of encoder-level representations. Our analysis of the learned representations reveals that character-based LSTM seems to be better at capturing morphological aspects compared to character-based CNN. We also show that a hard-attentional model provides better character-level re..

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